Enables you to audit your AI agent skills by running each one against an agent that cannot see it, diffing the resulting artifacts, and grading whether each skill genuinely improves, changes nothing, or worsens the output.
Provides advanced evaluation tools for assessing AI safety, alignment, and performance of LLM outputs. Enables programmatic evaluation of quality, safety metrics like toxicity and PII detection, and operational metrics including carbon footprint and cost estimation.
Enables LLMs to audit data drift and model degradation in tabular ML pipelines through deterministic statistical tests such as Kolmogorov-Smirnov and Population Stability Index, plus reusable prompts and standards resources.
Audits local datasets and ML training code for target, temporal, and entity leakage, PII exposure, and invalid evaluation design, returning structured evidence and pass/review/block verdicts before model training.
MCP-native auditor for LLM hallucination and grounding issues in RAG systems. Provides prioritized findings in table, JSON, or SARIF format for CI gating and AI agent integration.
Eval-integrity statistics for AI benchmark claims — multiple-testing correction, power/MDE for model gaps, judge-bias and leaderboard-rank checks. Catches a
benchmark number that won't survive a second look.
